File size: 7,521 Bytes
3afb283 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 |
#!/bin/python3
# ============================================================================
# Jimut Bahan Pal
# May, 11, 2021
# A script to collect all the slides and convert to a classification dataset
# by parsing each of the annotation files from each of the folders.
# Please run this in the same directory as RV-PBS folder.
# ============================================================================
import os
import cv2
import json
import glob
import math
import argparse
import shutil
import numpy as np
from lxml import etree
from tqdm import tqdm
from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
# Make the classification dataset's directory
SAVE_FOLDER_NAME = 'classification_data'
if not os.path.exists(SAVE_FOLDER_NAME):
os.makedirs(SAVE_FOLDER_NAME)
def rgba2rgb( rgba, background=(0,0,0) ):
"""
Converts a given rgba image to rgb
"""
row, col, ch = rgba.shape
if ch == 3:
return rgba
assert ch == 4, 'RGBA image has 4 channels.'
rgb = np.zeros( (row, col, 3), dtype='float32' )
r, g, b, a = rgba[:,:,0], rgba[:,:,1], rgba[:,:,2], rgba[:,:,3]
a = np.asarray( a, dtype='float32' ) / 255.0
R, G, B = background
rgb[:,:,0] = r * a + (1.0 - a) * R
rgb[:,:,1] = g * a + (1.0 - a) * G
rgb[:,:,2] = b * a + (1.0 - a) * B
return np.asarray( rgb, dtype='uint8')
def parse_anno_file(cvat_xml,image_name):
"""
Parses annotation file and returns the details of annotation
for the given image ID
"""
root = etree.parse(cvat_xml).getroot()
# print(root)
anno = []
image_name_attr = ".//image[@name='{}']".format(image_name)
for image_tag in root.iterfind(image_name_attr):
# print("Image tag = ",image_tag)
image = {}
for key, value in image_tag.items():
image[key] = value
image['shapes'] = []
for poly_tag in image_tag.iter('polygon'):
polygon = {'type': 'polygon'}
for key, value in poly_tag.items():
polygon[key] = value
image['shapes'].append(polygon)
for box_tag in image_tag.iter('box'):
box = {'type': 'box'}
for key, value in box_tag.items():
box[key] = value
box['points'] = "{0},{1};{2},{1};{2},{3};{0},{3}".format(
box['xtl'], box['ytl'], box['xbr'], box['ybr'])
# print("box = ",box)
image['shapes'].append(box)
image['shapes'].sort(key=lambda x: int(x.get('z_order', 0)))
anno.append(image)
# print("Annotation:",anno)
return anno
FOLDERS_LIST = ["MYELOCYTE", "BAND CELLS", "NEUTROPHILS", "BASOPHILS", "EOSINOPHILS", "PROMYELOCYTES", "BLAST CELLS", "LYMPHOCYTES", "METAMYELOCYTES", "MONOCYTES"]
folder_map = {}
rev_folder_map = {}
folder_map = {
"band": "BAND CELLS",
"basophil": "BASOPHILS",
"blast": "BLAST CELLS",
"eosinophil": "EOSINOPHILS",
"lymphocyte": "LYMPHOCYTES",
"metamyelocyte": "METAMYELOCYTES",
"monocyte": "MONOCYTES",
"myelocyte": "MYELOCYTE",
"neutrophil": "NEUTROPHILS",
"promyelocyte": "PROMYELOCYTES"
}
#print(folder_map)
for item in folder_map:
rev_folder_map[folder_map[item]] = item
#print(rev_folder_map)
all_folders_root = 'RV-PBS'
all_folders = glob.glob('{}/*'.format(all_folders_root))
for folders in tqdm(all_folders):
if folders.split('/')[-1] not in FOLDERS_LIST:
continue
# print(folders)
all_files_per_folder = glob.glob('{}/*'.format(folders))
#print(all_files_per_folder)
all_valid_files_per_folder = []
# annotation_file = ''
for files in all_files_per_folder:
#print(files)
get_extension = files.split('.')[-1]
#print(get_extension)
if get_extension == 'jpg' or get_extension == 'png':
all_valid_files_per_folder.append(files)
# elif get_extension == 'xml':
# annotation_file = files
#print("*"*40,get_extension)
# print(all_files_per_folder)
# print("**"*20,annotation_file)
file_name = folders+"/annotations.xml"
# print("oo"*50,file_name)
for valid_image_names in all_valid_files_per_folder:
subfolder_name = valid_image_names.split('/')[1]
#print("--"*50,subfolder_name)
subfolder_save = SAVE_FOLDER_NAME+"/"+rev_folder_map[subfolder_name]
if not os.path.exists(subfolder_save):
os.makedirs(subfolder_save)
valid_image_names_ = valid_image_names.split('/')[-1]
#print("Image Name = ",valid_image_names_)
annot = parse_anno_file(file_name,valid_image_names_)
#print("valid image names_ = ",valid_image_names_)
#print("Annotation = ",annot)
#print("--"*20)
try:
annot = annot[0]
except:
continue
# print(json.dumps(annot, indent=4, sort_keys=True))
im_height = annot['height']
im_width = annot['width']
im_id = annot['id']
im_name = annot['name']
im_shapes = annot['shapes']
# print(im_height)
# print(im_width)
# print(im_id)
# print("Annotation name = ",im_name)
name_ = im_name.split('.')[0]
# read image as RGB and add alpha (transparency)
im = Image.open(valid_image_names).convert("RGBA")
imArray = np.asarray(im)
count = 0
for shape in im_shapes:
count += 1
save_name = SAVE_FOLDER_NAME+"/"+rev_folder_map[subfolder_name]+"/"+name_+"_"+str(count)+".jpg"
# print("Save Name = ",save_name)
#print(shape)
points = shape['points']
#print(points)
all_points = points.split(';')
#print(all_points)
x_y = []
all_x = []
all_y = []
for point_ in all_points:
x = float(point_.split(',')[0])
y = float(point_.split(',')[1])
all_x.append(x)
all_y.append(y)
#print("X = ",x," Y = ",y)
x_y.append((x,y))
# print(x_y)
max_x = max(all_x)
min_x = min(all_x)
max_y = max(all_y)
min_y = min(all_y)
gap_x = max_x - min_x
gap_y = max_y - min_y
# print(max_x, " ", min_x, " ",max_y, " ",min_y)
maskIm = Image.new('L', (imArray.shape[1], imArray.shape[0]), 0)
ImageDraw.Draw(maskIm).polygon(x_y, outline=1, fill=1)
mask = np.array(maskIm)
# assemble new image (uint8: 0-255)
newImArray = np.empty(imArray.shape,dtype='uint8')
# colors (three first columns, RGB)
newImArray[:,:,:3] = imArray[:,:,:3]
# transparency (4th column)
newImArray[:,:,3] = mask*255
# plt.imshow(newImArray)
# plt.show()
img_extract = np.zeros((math.ceil(gap_x),math.ceil(gap_y),3))
img_extract = newImArray[math.ceil(min_y):math.ceil(max_y),math.ceil(min_x):math.ceil(max_x)]
# plt.imshow(img_extract)
# plt.show()
# back to Image from numpy
newIm = Image.fromarray(newImArray, "RGBA")
img_extract = rgba2rgb(img_extract)
# print(img_extract.shape)
cv2.imwrite(save_name,np.array(img_extract))
# newIm.save(save_name)
# break
|